The present invention relates generally to image reconstruction in positron emission tomography (PET) systems. More particularly, the present invention relates to the determination of normalization factors utilized in image reconstruction.
Various techniques or modalities may be used for medical imaging of, for example, portions of a patient's body. PET imaging is a non-invasive nuclear imaging technique that allows the study of the internal organs of a human body. PET imaging allows the physician to view the patient's entire body at the same time. PET imaging produces images of many functions of the human body that are otherwise unobtainable.
In PET imaging, positron emitting isotopes are injected into the patient's body. These isotopes are referred to as radiopharmaceuticals, which are short-lived unstable isotopes. Once injected into the body, these isotopes decay and discharge positively charged particles called positrons. Upon being discharged, when these positrons encounter an electron, they are annihilated and converted into a pair of photons. The two photons are emitted in nearly opposite directions. A PET scanner typically includes several coaxial rings of detectors around the patient's body for detecting such annihilation events.
The detectors include crystals or scintillators to sense the scintillation of photons or gamma rays colliding with them. Coincidence detection circuits connected to the detectors record only those photons that are detected simultaneously by two detectors on opposite sides of the patient. During a typical scan, hundreds of millions of events are detected and recorded to indicate the number of annihilation events along lines joining pairs of detectors in the ring. The collected data is then used to reconstruct an image.
The existing PET scanners are based on either two-dimensional (2D) or three-dimensional (3D) data acquisition. In the case of 2D acquisition PET scanners, data is collected only along planes perpendicular to the central axis of the scanner. In order to collect data only from a single plane, the detector rings of the 2D PET scanners are separated by short septa or detector shields.
In the case of 3D PET scanners, the septa between the detector rings are removed. Data is collected throughout the sample volume and then reconstructed based on its actual trajectory through image space. These trajectories may or may not exist in one particular plane.
The data collected during a scan may contain inconsistencies. These inconsistencies may arise due to different factors, or the operating characteristics of the imaging systems, one of them being the presence of shields or septa between the detector rings of the PET scanner. The collected data is therefore normalized prior to using such data for reconstruction of the image. In accordance with the known methods for the normalization of scan data, correction factor is a product of (i) the single-crystal efficiencies of the two detectors forming the coincidence, and (ii) a geometric factor.
Several normalization methods are used to account for the differences in detection efficiency among the lines of response (LORs) in the system. Existing normalization methods account for the LOR radius and transaxial angle, but generally do not take the axial angle of the detector line or response into account. In at least one known method, the geometric factor is assumed to be a function of an LOR radius and an LOR angle in a single plane parallel to the detector rings. In 2D scanning, all the data is in the plane and the axial angle is zero. In 3D PET scanners, the absence of septa generally makes the response of the system independent of the axial angle. When septa are added to the 3D system, however, this is no longer true. Therefore, there exist significant axial angle effects.
These known methods for the normalization of scan data have several disadvantages. For example, in the case of 2D scanning, minor errors in the positioning of the septa of one millimeter (mm) or less can cause significant inaccuracies in the scan data. In many applications, such positioning errors are likely to occur. Further, in the case of 3D acquisition with the septa removed, the system records a larger number of false coincidence events and scattered photons. This increases noise in the image and therefore reduces image quality. When the septa are introduced between the detector rings of a 3D PET scanner there is a substantial geometric factor in the axial direction. There is no compensation for this substantial geometric factor in the known methods of image reconstruction.
Therefore, known image reconstruction methods fail to take into account the presence of septa for the purpose of the normalization in case of 3D PET scanners. Further, the known normalization methods do not compensate for the geometric factor that arises in the axial direction.